BUG: Fixed bug, where pandas._libs.lib.maybe_convert_objects function improperly handled arrays with bools and NaNs

doc/source/whatsnew/v1.0.2.rst

@@ -24,6 +24,7 @@ Fixed regressions
 - Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
 - Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
 - Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`)
+- Fixed bug, where the ``Series`` representation of an object ``Index`` with bools and ``NaN`` values was wrong (:issue:`32146`)
 -
 
 .. ---------------------------------------------------------------------------

doc/source/whatsnew/v1.1.0.rst

@@ -123,6 +123,7 @@ Performance improvements
 Bug fixes
 ~~~~~~~~~
 
+-
 
 Categorical
 ^^^^^^^^^^^

pandas/_libs/lib.pyx

@@ -2300,7 +2300,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0,
                                 return uints
                             else:
                                 return ints
-                elif seen.is_bool:
+                elif seen.is_bool and not seen.nan_:
                     return bools.view(np.bool_)
 
     return objects

pandas/tests/dtypes/test_inference.py

@@ -568,6 +568,43 @@ def test_maybe_convert_objects_nullable_integer(self, exp):
 
         tm.assert_extension_array_equal(result, exp)
 
+    def test_maybe_convert_objects_bool_nan(self):
+        # GH32146
+        arr = pd.Index([True, False, np.nan], dtype=object)
+        exp = np.array([True, False, np.nan], dtype=object)
+        out = lib.maybe_convert_objects(arr.values, safe=1)
+        tm.assert_numpy_array_equal(out, exp)
+
+    @pytest.mark.parametrize(
+        "arr, dropna, exp",
+        [
+            (
+                pd.Series([False, True, True, pd.NA]),
+                False,
+                pd.Series([2, 1, 1], index=[True, False, pd.NA]),
+            ),
+            (
+                pd.Series([False, True, True, pd.NA]),
+                True,
+                pd.Series([2, 1], index=[True, False]),
+            ),
+            (
+                pd.Series(range(3), index=[True, False, np.nan]).index,
+                False,
+                pd.Series([1, 1, 1], index=[True, False, pd.NA]),
+            ),
+        ],
+    )
+    def test_maybe_convert_objects_value_counts(self, arr, dropna, exp):
+        out = arr.value_counts(dropna=dropna)
+        tm.assert_series_equal(out, exp)
+
+    def test_maybe_convert_objects_index_representation(self):
+        arr = pd.Index([True, False, np.nan], dtype=object)
+        exp = arr.format()
+        out = ["True", "False", "NaN"]
+        assert out == exp
+
     def test_mixed_dtypes_remain_object_array(self):
         # GH14956
         array = np.array([datetime(2015, 1, 1, tzinfo=pytz.utc), 1], dtype=object)